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Shetty A, Delanerolle G, Zeng Y, Shi JQ, Ebrahim R, Pang J, Hapangama D, Sillem M, Shetty S, Shetty B, Hirsch M, Raymont V, Majumder K, Chong S, Goodison W, O’Hara R, Hull L, Pluchino N, Shetty N, Elneil S, Fernandez T, Brownstone RM, Phiri P. A systematic review and meta-analysis of digital application use in clinical research in pain medicine. Front Digit Health 2022; 4:850601. [PMID: 36405414 PMCID: PMC9668017 DOI: 10.3389/fdgth.2022.850601] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 10/07/2022] [Indexed: 01/18/2023] Open
Abstract
IMPORTANCE Pain is a silent global epidemic impacting approximately a third of the population. Pharmacological and surgical interventions are primary modes of treatment. Cognitive/behavioural management approaches and interventional pain management strategies are approaches that have been used to assist with the management of chronic pain. Accurate data collection and reporting treatment outcomes are vital to addressing the challenges faced. In light of this, we conducted a systematic evaluation of the current digital application landscape within chronic pain medicine. OBJECTIVE The primary objective was to consider the prevalence of digital application usage for chronic pain management. These digital applications included mobile apps, web apps, and chatbots. DATA SOURCES We conducted searches on PubMed and ScienceDirect for studies that were published between 1st January 1990 and 1st January 2021. STUDY SELECTION Our review included studies that involved the use of digital applications for chronic pain conditions. There were no restrictions on the country in which the study was conducted. Only studies that were peer-reviewed and published in English were included. Four reviewers had assessed the eligibility of each study against the inclusion/exclusion criteria. Out of the 84 studies that were initially identified, 38 were included in the systematic review. DATA EXTRACTION AND SYNTHESIS The AMSTAR guidelines were used to assess data quality. This assessment was carried out by 3 reviewers. The data were pooled using a random-effects model. MAIN OUTCOMES AND MEASURES Before data collection began, the primary outcome was to report on the standard mean difference of digital application usage for chronic pain conditions. We also recorded the type of digital application studied (e.g., mobile application, web application) and, where the data was available, the standard mean difference of pain intensity, pain inferences, depression, anxiety, and fatigue. RESULTS 38 studies were included in the systematic review and 22 studies were included in the meta-analysis. The digital interventions were categorised to web and mobile applications and chatbots, with pooled standard mean difference of 0.22 (95% CI: -0.16, 0.60), 0.30 (95% CI: 0.00, 0.60) and -0.02 (95% CI: -0.47, 0.42) respectively. Pooled standard mean differences for symptomatologies of pain intensity, depression, and anxiety symptoms were 0.25 (95% CI: 0.03, 0.46), 0.30 (95% CI: 0.17, 0.43) and 0.37 (95% CI: 0.05, 0.69), respectively. A sub-group analysis was conducted on pain intensity due to the heterogeneity of the results (I 2 = 82.86%; p = 0.02). After stratifying by country, we found that digital applications were more likely to be effective in some countries (e.g., United States, China) than others (e.g., Ireland, Norway). CONCLUSIONS AND RELEVANCE The use of digital applications in improving pain-related symptoms shows promise, but further clinical studies would be needed to develop more robust applications. SYSTEMATIC REVIEW REGISTRATION https://www.crd.york.ac.uk/prospero/, identifier: CRD42021228343.
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Affiliation(s)
- Ashish Shetty
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Gayathri Delanerolle
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Yutian Zeng
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China,Alan Turing Institute, London, United Kingdom
| | - Jian Qing Shi
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China,Alan Turing Institute, London, United Kingdom
| | - Rawan Ebrahim
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Joanna Pang
- Research & Innovation Department, Southern Health NHS Foundation Trust, Southampton, United Kingdom
| | - Dharani Hapangama
- Department of Women and Children’s Health, Liverpool Women’s NHS Foundation, Liverpool, United Kingdom
| | - Martin Sillem
- Praxisklinik am Rosengarten Mannheim, Saarland University Medical Centre, Homburg, Germany
| | | | | | - Martin Hirsch
- Queen Square Institute of Neurology, University College London, London, United Kingdom,Oxford University Hospitals NHS Foundation Trust, Gynaecology, Oxford, United Kingdom
| | - Vanessa Raymont
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Kingshuk Majumder
- University of Manchester NHS Foundation Trust, Gynaecology, Manchester, United Kingdom
| | - Sam Chong
- University College London Hospitals NHS Foundation Trust, London, United Kingdom,Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - William Goodison
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Rebecca O’Hara
- Robinson Research Institute, University of Adelaide, Adelaide, Australia
| | - Louise Hull
- Robinson Research Institute, University of Adelaide, Adelaide, Australia
| | | | - Naresh Shetty
- Department of Orthopedics, M.S. Ramaiah Medical College, Bangalore, India
| | - Sohier Elneil
- University College London Hospitals NHS Foundation Trust, London, United Kingdom,Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Tacson Fernandez
- Queen Square Institute of Neurology, University College London, London, United Kingdom,Chronic Pain Medicine, Royal National Orthopaedic Hospital, London, United Kingdom
| | - Robert M. Brownstone
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter Phiri
- Research & Innovation Department, Southern Health NHS Foundation Trust, Southampton, United Kingdom,Primary Care, Population Sciences and Medical Education Division, University of Southampton, Southampton, United Kingdom,Correspondence: Peter Phiri
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The promise of a model-based psychiatry: building computational models of mental ill health. Lancet Digit Health 2022; 4:e816-e828. [PMID: 36229345 PMCID: PMC9627546 DOI: 10.1016/s2589-7500(22)00152-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/05/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
Abstract
Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.
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Potier R. Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'ÉVOLUTION PSYCHIATRIQUE 2022. [DOI: 10.1016/j.evopsy.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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54
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Benrimoh D, Chheda FD, Margolese HC. The Best Predictor of the Future-the Metaverse, Mental Health, and Lessons Learned From Current Technologies. JMIR Ment Health 2022; 9:e40410. [PMID: 36306155 PMCID: PMC9652728 DOI: 10.2196/40410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/13/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
The metaverse-a virtual world accessed via virtual reality technology-has been heralded as the next key digital experience. It is meant to provide the next evolution of human interaction after social media and telework. However, in the context of the growing awareness of the risks to mental health posed by current social media technologies, there is a great deal of uncertainty as to the potential effects of this new technology on mental health. This uncertainty is compounded by a lack of clarity regarding what form the metaverse will ultimately take and how widespread its application will be. Despite this, given the nascent state of the metaverse, there is an opportunity to plan the research and regulatory approaches needed to understand it and promote its positive effects while protecting vulnerable groups. In this viewpoint, we examine the following three current technologies whose functions comprise a portion of what the metaverse seeks to accomplish: teleworking, virtual reality, and social media. We attempted to understand in what ways the metaverse may have similar benefits and pitfalls to these technologies but also how it may fundamentally differ from them. These differences suggest potential research questions to be addressed in future work. We found that current technologies have enabled tools such as virtual reality-assisted therapy, avatar therapy, and teletherapy, which have had positive effects on mental health care, and that the metaverse may provide meaningful improvements to these tools. However, given its similarities to social media and its expansion upon the social media experience, the metaverse raises some of the same concerns that we have with social media, such as the possible exacerbation of certain mental health problems. These concerns led us to consider questions such as how the users will be protected and what regulatory mechanisms will be put in place to ensure user safety. Although clear answers to these questions are challenging in this early phase of metaverse research, in this viewpoint, we use the context provided by comparator technologies to provide recommendations to maximize the potential benefits and limit the putative harms of the metaverse. We hope that this paper encourages discussions among researchers and policy makers.
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Affiliation(s)
- David Benrimoh
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Forum D Chheda
- McGill University Healthcare Center, Montreal, QC, Canada
| | - Howard C Margolese
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,McGill University Healthcare Center, Montreal, QC, Canada
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Nguyen B, Ivanov M, Bhat V, Krishnan S. Digital phenotyping for classification of anxiety severity during COVID-19. Front Digit Health 2022; 4:877762. [PMID: 36310921 PMCID: PMC9612961 DOI: 10.3389/fdgth.2022.877762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 09/14/2022] [Indexed: 11/07/2022] Open
Abstract
COVID-19 has led to an increase in anxiety among Canadians. Canadian Perspectives Survey Series (CPSS) is a dataset created by Statistics Canada to monitor the effects of COVID-19 among Canadians. Survey data were collected to evaluate health and health-related behaviours. This work evaluates CPSS2 and CPSS4, which were collected in May and July of 2020, respectively. The survey data consist of up to 102 questions. This work proposes the use of the survey data characteristics to identify the level of anxiety within the Canadian population during the first- and second-phases of COVID-19 and is validated by using the General Anxiety Disorder (GAD)-7 questionnaire. Minimum redundancy maximum relevance (mRMR) is applied to select the top features to represent user anxiety, and support vector machine (SVM) is used to classify the separation of anxiety severity. We employ SVM for binary classification with 10-fold cross validation to separate the labels of Minimal and Severe anxiety to achieve an overall accuracy of 94.77±0.13% and 97.35±0.11% for CPSS2 and CPSS4, respectively. After analysis, we compared the results of the first and second phases of COVID-19 and determined a subset of the features that could be represented as pseudo passive (PP) data. The accurate classification provides a proxy on the potential onsets of anxiety to provide tailored interventions. Future works can augment the proposed PP data for carrying out a more detailed digital phenotyping.
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Affiliation(s)
- Binh Nguyen
- Signal Analysis Research (SAR) Group, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada,Correspondence: Binh Nguyen
| | - Martin Ivanov
- Signal Analysis Research (SAR) Group, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
| | - Venkat Bhat
- Signal Analysis Research (SAR) Group, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada,Interventional Psychiatry Program, St. Michael’s Hospital, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sri Krishnan
- Signal Analysis Research (SAR) Group, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
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56
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Fahed M, McManus K, Vahia IV, Offodile AC. Digital Phenotyping of Behavioral Symptoms as the Next Frontier for Personalized and Proactive Cancer Care. JCO Clin Cancer Inform 2022; 6:e2200095. [DOI: 10.1200/cci.22.00095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Mario Fahed
- Department of Psychiatry, University of Connecticut, Farmington, CT
- Department of Psychiatry, Yale University, New Haven, CT
| | - Kaitlin McManus
- Division of Geriatric Psychiatry, McLean Hospital, Belmont, MA
| | - Ipsit V. Vahia
- Division of Geriatric Psychiatry, McLean Hospital, Belmont, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Anaeze C. Offodile
- Department of Plastic Surgery, UT MD Anderson Cancer Center, Houston, TX
- Department of Health Services Research, UT MD Anderson Cancer Center, Houston, TX
- Baker Institute for Public Policy, Rice University, Houston, TX
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57
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Birk RH, Samuel G. Digital Phenotyping for Mental Health: Reviewing the Challenges of Using Data to Monitor and Predict Mental Health Problems. Curr Psychiatry Rep 2022; 24:523-528. [PMID: 36001220 DOI: 10.1007/s11920-022-01358-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW We review recent developments within digital phenotyping for mental health, a field dedicated to using digital data for diagnosing, predicting, and monitoring mental health problems. We especially focus on recent critiques and challenges to digital phenotyping from within the social sciences. RECENT FINDINGS Three significant strands of criticism against digital phenotyping for mental health have been developed within the social sciences. This literature problematizes the idea that digital data can be objective, that it can be unbiased, and argues that it has multiple ethical and practical challenges. Digital phenotyping for mental health is a rapidly growing and developing field, but with considerable challenges that are not easily solvable. This includes when, and if, data from digital phenotyping is actionable in practice; the involvement of user and patient perspectives in digital phenotyping research; the possibility of biased data; and challenges to the idea that digital phenotyping can be more objective than other forms of psychiatric assessment.
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Affiliation(s)
- Rasmus H Birk
- Department of Communication & Psychology, Aalborg University, Aalborg, Denmark.
| | - Gabrielle Samuel
- Department of Global Health & Social Medicine, King's College London, London, UK
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58
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Stein DJ, Shoptaw SJ, Vigo DV, Lund C, Cuijpers P, Bantjes J, Sartorius N, Maj M. Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration. World Psychiatry 2022; 21:393-414. [PMID: 36073709 PMCID: PMC9453916 DOI: 10.1002/wps.20998] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Psychiatry has always been characterized by a range of different models of and approaches to mental disorder, which have sometimes brought progress in clinical practice, but have often also been accompanied by critique from within and without the field. Psychiatric nosology has been a particular focus of debate in recent decades; successive editions of the DSM and ICD have strongly influenced both psychiatric practice and research, but have also led to assertions that psychiatry is in crisis, and to advocacy for entirely new paradigms for diagnosis and assessment. When thinking about etiology, many researchers currently refer to a biopsychosocial model, but this approach has received significant critique, being considered by some observers overly eclectic and vague. Despite the development of a range of evidence-based pharmacotherapies and psychotherapies, current evidence points to both a treatment gap and a research-practice gap in mental health. In this paper, after considering current clinical practice, we discuss some proposed novel perspectives that have recently achieved particular prominence and may significantly impact psychiatric practice and research in the future: clinical neuroscience and personalized pharmacotherapy; novel statistical approaches to psychiatric nosology, assessment and research; deinstitutionalization and community mental health care; the scale-up of evidence-based psychotherapy; digital phenotyping and digital therapies; and global mental health and task-sharing approaches. We consider the extent to which proposed transitions from current practices to novel approaches reflect hype or hope. Our review indicates that each of the novel perspectives contributes important insights that allow hope for the future, but also that each provides only a partial view, and that any promise of a paradigm shift for the field is not well grounded. We conclude that there have been crucial advances in psychiatric diagnosis and treatment in recent decades; that, despite this important progress, there is considerable need for further improvements in assessment and intervention; and that such improvements will likely not be achieved by any specific paradigm shifts in psychiatric practice and research, but rather by incremental progress and iterative integration.
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Affiliation(s)
- Dan J. Stein
- South African Medical Research Council Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape TownCape TownSouth Africa
| | - Steven J. Shoptaw
- Division of Family MedicineDavid Geffen School of Medicine, University of California Los AngelesLos AngelesCAUSA
| | - Daniel V. Vigo
- Department of PsychiatryUniversity of British ColumbiaVancouverBCCanada
| | - Crick Lund
- Centre for Global Mental Health, Health Service and Population Research DepartmentInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental PsychologyAmsterdam Public Health Research Institute, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Jason Bantjes
- Alcohol, Tobacco and Other Drug Research UnitSouth African Medical Research CouncilCape TownSouth Africa
| | - Norman Sartorius
- Association for the Improvement of Mental Health ProgrammesGenevaSwitzerland
| | - Mario Maj
- Department of PsychiatryUniversity of Campania “L. Vanvitelli”NaplesItaly
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59
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Dunne DM, Lefevre-Lewis C, Cunniffe B, Impey SG, Tod D, Close GL, Morton JP, Murphy R. Athlete experiences of communication strategies in applied sports nutrition and future considerations for mobile app supportive solutions. Front Sports Act Living 2022; 4:911412. [PMID: 36172339 PMCID: PMC9512279 DOI: 10.3389/fspor.2022.911412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
AimThis study aimed to explore athletes' experiences and opinions of communication strategies in applied sports nutrition, as well as capture suggestions for future mobile app supportive solutions.MethodsA qualitative approach was used for this research. Data was generated from semi-structured focus groups (n = 9) with a purposive sample of 41 (male = 24, female = 17) full time professional athletes (mean age 24 ± 4.59) from five sports (football, rugby union, athletics, cycling, and boxing). Data was analyzed using reflexive thematic analysis.ResultsThe analysis identified four higher order themes and five sub themes. Athletes appear dissatisfied with the levels of personalization in the nutrition support they receive. Limited practitioner contact time was suggested as a contributing factor to this problem. Athletes acknowledged the usefulness of online remote nutrition support and reported a desire for more personalized technology that can tailor support to their individual needs.ConclusionAthletes experienced a hybrid human-computer approach that combines in-person and remote digital methods to communicate with and receive information from practitioners. Mobile technology may now afford sports nutritionists with new opportunities to develop scalable solutions to support practice.
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Affiliation(s)
- David Mark Dunne
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
- *Correspondence: David Mark Dunne
| | | | - Brian Cunniffe
- Department of Surgery, University College London, London, United Kingdom
- English Institute of Sport, Manchester, United Kingdom
| | - Samuel George Impey
- Centre for Exercise and Sports Science Research, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - David Tod
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
| | - Graeme Leonard Close
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
| | - James P. Morton
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
| | - Rebecca Murphy
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
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60
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Ho SM, Liu X, Seraj MS, Dickey S. Social distance "nudge:" a context aware mHealth intervention in response to COVID pandemics. COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY 2022; 29:1-24. [PMID: 36106126 PMCID: PMC9461402 DOI: 10.1007/s10588-022-09365-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
The impact of the COVID pandemic to our society is unprecedented in our time. As coronavirus mutates, maintaining social distance remains an essential step in defending personal as well as public health. This study conceptualizes the social distance "nudge" and explores the efficacy of mHealth digital intervention, while developing and validating a choice architecture that aims to influence users' behavior in maintaining social distance for their own self-interest. End-user nudging experiments were conducted via a mobile phone app that was developed as a research artifact. The accuracy of social distance nudging was validated in both United States and Japan. Future work will consider behavioral studies to better understand the effectiveness of this digital nudging intervention.
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Affiliation(s)
- Shuyuan Mary Ho
- School of Information, Florida State University, 142 Collegiate Loop, P.O. Box 3062100, Tallahassee, FL 32306-2100 USA
| | - Xiuwen Liu
- Department of Computer Science, Florida State University, 1017 Academy Way, Tallahassee, FL 32304 USA
| | - Md Shamim Seraj
- Department of Computer Science, Florida State University, 1017 Academy Way, Tallahassee, FL 32304 USA
| | - Sabrina Dickey
- College of Nursing, Florida State University, 98 Varsity Way, Tallahassee, FL 32306-4310 USA
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61
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Lui GY, Loughnane D, Polley C, Jayarathna T, Breen PP. The Apple Watch for Monitoring Mental Health-Related Physiological Symptoms: Literature Review. JMIR Ment Health 2022; 9:e37354. [PMID: 36069848 PMCID: PMC9494213 DOI: 10.2196/37354] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 07/29/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods of care to meet demand, given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support their effectiveness in mental health contexts. OBJECTIVE This literature review aims to identify current and potential physiological or physiologically related monitoring capabilities of the Apple Watch relevant to mental health monitoring and examine the accuracy and validation status of these measures and their implications for mental health treatment. METHODS A literature review was conducted from June 2021 to July 2021 of both published and gray literature pertaining to the Apple Watch, mental health, and physiology. The literature review identified studies validating the sensor capabilities of the Apple Watch. RESULTS A total of 5583 paper titles were identified, with 115 (2.06%) reviewed in full. Of these 115 papers, 19 (16.5%) were related to Apple Watch validation or comparison studies. Most studies showed that the Apple Watch could measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with the Apple Watch generally providing the best results compared with peers, despite overestimation. Heart rate variability measurements were found to have gaps in data but were able to detect mild mental stress. Activity monitoring with step counting showed good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial fibrillation detection showed mixed results, in part because of a high inconclusive result rate, but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature; however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep. CONCLUSIONS The results are encouraging regarding the application of the Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefits may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, a supportive health economic analysis, and concerns about personal health information remain key factors that must be addressed to enable broader uptake.
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Affiliation(s)
- Gough Yumu Lui
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | | | - Caitlin Polley
- Electrical and Electronic Engineering, School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW, Australia
| | - Titus Jayarathna
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | - Paul P Breen
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia.,Translational Health Research Institute, Western Sydney University, Penrith, NSW, Australia
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Fujio K, Inomata T, Fujisawa K, Sung J, Nakamura M, Iwagami M, Muto K, Ebihara N, Nakamura M, Okano M, Akasaki Y, Okumura Y, Ide T, Nojiri S, Nagao M, Fujimoto K, Hirosawa K, Murakami A. Patient and public involvement in mobile health-based research for hay fever: a qualitative study of patient and public involvement implementation process. RESEARCH INVOLVEMENT AND ENGAGEMENT 2022; 8:45. [PMID: 36056430 PMCID: PMC9437402 DOI: 10.1186/s40900-022-00382-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Smartphones are being increasingly used for research owing to their multifunctionality and flexibility, and crowdsourced research using smartphone applications (apps) is effective in the early detection and management of chronic diseases. We developed the AllerSearch app to gather real-world data on individual subjective symptoms and lifestyle factors related to hay fever. This study established a foundation for interactive research by adopting novel, diverse perspectives accrued through implementing the principles of patient and public involvement (PPI) in the development of our app. METHODS Patients and members of the public with a history or family history of hay fever were recruited from November 2019 to December 2021 through a dedicated website, social networking services, and web briefing according to the PPI Guidebook 2019 by the Japan Agency for Medical Research and Development. Nine opinion exchange meetings were held from February 2020 to December 2021 to collect opinions and suggestions for updating the app. After each meeting, interactive evaluations from PPI contributors and researchers were collected. The compiled suggestions were then incorporated into the app, establishing an active feedback loop fed by the consistently interactive infrastructure. RESULTS Four PPI contributors (one man and three women) were recruited, and 93 items were added/changed in the in-app survey questionnaire in accordance with discussions from the exchange meetings. The exchange meetings emphasized an atmosphere and opportunity for participants to speak up, ensuring frequent opportunities for them to contribute to the research. In March 2020, a public website was created to display real-time outcomes of the number of participants and users' hay-fever-preventative behaviors. In August 2020, a new PPI-implemented AllerSearch app was released. CONCLUSIONS This study marks the first research on clinical smartphone apps for hay fever in Japan that implements PPI throughout its timeline from research and development to the publication of research results. Taking advantage of the distinct perspectives offered by PPI contributors, a step was taken toward actualizing a foundation for an interactive research environment. These results should promote future PPI research and foster the establishment of a social construct that enables PPI efforts in various fields.
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Affiliation(s)
- Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takenori Inomata
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan.
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
| | - Kumiko Fujisawa
- Department of Public Policy, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
| | - Masahiro Nakamura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Kaori Muto
- Department of Public Policy, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Nobuyuki Ebihara
- Department of Ophthalmology, Urayasu Hospital, Juntendo University, Chiba, Japan
| | - Masahiro Nakamura
- Department of Otorhinolaryngology, Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Mitsuhiro Okano
- Department of Otorhinolaryngology, International University of Health and Welfare, Narita, Japan
| | - Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takuma Ide
- Department of Otorhinolaryngology, Head and Neck Surgery, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Shuko Nojiri
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
| | - Masashi Nagao
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
- Department of Orthopedic Surgery, Juntendo University Graduate School of Medicine, Tokyo, Japan
- School of Health and Sports Science, Juntendo University, Chiba, Japan
| | - Keiichi Fujimoto
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akira Murakami
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0033, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
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Choudhary S, Thomas N, Alshamrani S, Srinivasan G, Ellenberger J, Nawaz U, Cohen R. A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study. JMIR Med Inform 2022; 10:e38943. [PMID: 36040777 PMCID: PMC9472035 DOI: 10.2196/38943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/11/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Anxiety is one of the leading causes of mental health disability around the world. Currently, a majority of the population who experience anxiety go undiagnosed or untreated. New and innovative ways of diagnosing and monitoring anxiety have emerged using smartphone sensor-based monitoring as a metric for the management of anxiety. This is a novel study as it adds to the field of research through the use of nonidentifiable smartphone usage to help detect and monitor anxiety remotely and in a continuous and passive manner. OBJECTIVE This study aims to evaluate the accuracy of a novel mental behavioral profiling metric derived from smartphone usage for the identification and tracking of generalized anxiety disorder (GAD). METHODS Smartphone data and self-reported 7-item GAD anxiety assessments were collected from 229 participants using an Android operating system smartphone in an observational study over an average of 14 days (SD 29.8). A total of 34 features were mined to be constructed as a potential digital phenotyping marker from continuous smartphone usage data. We further analyzed the correlation of these digital behavioral markers against each item of the 7-item Generalized Anxiety Disorder Scale (GAD-7) and its influence on the predictions of machine learning algorithms. RESULTS A total of 229 participants were recruited in this study who had completed the GAD-7 assessment and had at least one set of passive digital data collected within a 24-hour period. The mean GAD-7 score was 11.8 (SD 5.7). Regression modeling was tested against classification modeling and the highest prediction accuracy was achieved from a binary XGBoost classification model (precision of 73%-81%; recall of 68%-87%; F1-score of 71%-79%; accuracy of 76%; area under the curve of 80%). Nonparametric permutation testing with Pearson correlation results indicated that the proposed metric (Mental Health Similarity Score [MHSS]) had a colinear relationship between GAD-7 Items 1, 3 and 7. CONCLUSIONS The proposed MHSS metric demonstrates the feasibility of using passively collected nonintrusive smartphone data and machine learning-based data mining techniques to track an individuals' daily anxiety levels with a 76% accuracy that directly relates to the GAD-7 scale.
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Affiliation(s)
- Soumya Choudhary
- Department of Research, Behavidence, Inc., New York, NY, United States
| | - Nikita Thomas
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | - Sultan Alshamrani
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | - Girish Srinivasan
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | | | - Usman Nawaz
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | - Roy Cohen
- Department of Research, Behavidence, Inc., New York, NY, United States
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Deveau N, Washington P, Leblanc E, Husic A, Dunlap K, Penev Y, Kline A, Mutlu OC, Wall DP. Machine learning models using mobile game play accurately classify children with autism. INTELLIGENCE-BASED MEDICINE 2022; 6:100057. [PMID: 36035501 PMCID: PMC9398788 DOI: 10.1016/j.ibmed.2022.100057] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 01/10/2022] [Accepted: 03/29/2022] [Indexed: 11/23/2022]
Abstract
Digitally-delivered healthcare is well suited to address current inequities in the delivery of care due to barriers of access to healthcare facilities. As the COVID-19 pandemic phases out, we have a unique opportunity to capitalize on the current familiarity with telemedicine approaches and continue to advocate for mainstream adoption of remote care delivery. In this paper, we specifically focus on the ability of GuessWhat? a smartphone-based charades-style gamified therapeutic intervention for autism spectrum disorder (ASD) to generate a signal that distinguishes children with ASD from neurotypical (NT) children. We demonstrate the feasibility of using "in-the-wild", naturalistic gameplay data to distinguish between ASD and NT by children by training a random forest classifier to discern the two classes (AU-ROC = 0.745, recall = 0.769). This performance demonstrates the potential for GuessWhat? to facilitate screening for ASD in historically difficult-to-reach communities. To further examine this potential, future work should expand the size of the training sample and interrogate differences in predictive ability by demographic.
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Affiliation(s)
- Nicholas Deveau
- Biomedical Data Science, Stanford University, Stanford, 94305, California, United States
| | - Peter Washington
- Bioengineering, Stanford University, Stanford, 94305, California, United States
| | - Emilie Leblanc
- Pediatrics, Stanford University, Stanford, 94305, California, United States
| | - Arman Husic
- Pediatrics, Stanford University, Stanford, 94305, California, United States
| | - Kaitlyn Dunlap
- Pediatrics, Stanford University, Stanford, 94305, California, United States
| | - Yordan Penev
- Pediatrics, Stanford University, Stanford, 94305, California, United States
| | - Aaron Kline
- Pediatrics, Stanford University, Stanford, 94305, California, United States
| | - Onur Cezmi Mutlu
- Electrical Engineering, Stanford University, Stanford, 94305, California, United States
| | - Dennis P Wall
- Biomedical Data Science, Stanford University, Stanford, 94305, California, United States
- Pediatrics, Stanford University, Stanford, 94305, California, United States
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Chikersal P, Venkatesh S, Masown K, Walker E, Quraishi D, Dey A, Goel M, Xia Z. Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping. JMIR Ment Health 2022; 9:e38495. [PMID: 35849686 PMCID: PMC9407162 DOI: 10.2196/38495] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 07/15/2022] [Accepted: 07/16/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has broad negative impact on the physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS). OBJECTIVE We presented a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict their health outcomes in a natural experiment during a state-mandated stay-at-home period due to a global pandemic. METHODS First, we extracted features that capture behavior changes due to the stay-at-home order. Then, we adapted and applied an existing algorithm to these behavior-change features to predict the presence of depression, high global MS symptom burden, severe fatigue, and poor sleep quality during the stay-at-home period. RESULTS Using data collected between November 2019 and May 2020, the algorithm detected depression with an accuracy of 82.5% (65% improvement over baseline; F1-score: 0.84), high global MS symptom burden with an accuracy of 90% (39% improvement over baseline; F1-score: 0.93), severe fatigue with an accuracy of 75.5% (22% improvement over baseline; F1-score: 0.80), and poor sleep quality with an accuracy of 84% (28% improvement over baseline; F1-score: 0.84). CONCLUSIONS Our approach could help clinicians better triage patients with MS and potentially other chronic neurological disorders for interventions and aid patient self-monitoring in their own environment, particularly during extraordinarily stressful circumstances such as pandemics, which would cause drastic behavior changes.
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Affiliation(s)
- Prerna Chikersal
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Shruthi Venkatesh
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Karman Masown
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Elizabeth Walker
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Danyal Quraishi
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Anind Dey
- Information School, University of Washington, Seattle, Seattle, WA, United States
| | - Mayank Goel
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
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Kamstra RJM, Boorsma A, Krone T, van Stokkum RM, Eggink HM, Peters T, Pasman WJ. Validation of the Mobile App Version of the EQ-5D-5L Quality of Life Questionnaire Against the Gold Standard Paper-Based Version: Randomized Crossover Study. JMIR Form Res 2022; 6:e37303. [PMID: 35969437 PMCID: PMC9412727 DOI: 10.2196/37303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/03/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background Study participants and patients often perceive (long) questionnaires as burdensome. In addition, paper-based questionnaires are prone to errors such as (unintentionally) skipping questions or filling in a wrong type of answer. Such errors can be prevented with the emergence of mobile questionnaire apps. Objective This study aimed to validate an innovative way to measure the quality of life using a mobile app based on the EQ-5D-5L questionnaire. This validation study compared the EQ-5D-5L questionnaire requested by a mobile app with the gold standard paper-based version of the EQ-5D-5L. Methods This was a randomized, crossover, and open study. The main criteria for participation were participants should be aged ≥18 years, healthy at their own discretion, in possession of a smartphone with at least Android version 4.1 or higher or iOS version 9 or higher, digitally skilled in downloading the mobile app, and able to read and answer questionnaires in Dutch. Participants were recruited by a market research company that divided them into 2 groups balanced for age, gender, and education. Each participant received a digital version of the EQ-5D-5L questionnaire via a mobile app and the EQ-5D-5L paper-based questionnaire by postal mail. In the mobile app, participants received, for 5 consecutive days, 1 question in the morning and 1 question in the afternoon; as such, all questions were asked twice (at time point 1 [App T1] and time point 2 [App T2]). The primary outcomes were the correlations between the answers (scores) of each EQ-5D-5L question answered via the mobile app compared with the paper-based questionnaire to assess convergent validity. Results A total of 255 participants (healthy at their own discretion), 117 (45.9%) men and 138 (54.1%) women in the age range of 18 to 64 years, completed the study. To ensure randomization, the measured demographics were checked and compared between groups. To compare the results of the electronic and paper-based questionnaires, polychoric correlation analysis was performed. All questions showed a high correlation (0.64-0.92; P<.001) between the paper-based and the mobile app–based questions at App T1 and App T2. The scores and their variance remained similar over the questionnaires, indicating no clear difference in the answer tendency. In addition, the correlation between the 2 app-based questionnaires was high (>0.73; P<.001), illustrating a high test-retest reliability, indicating it to be a reliable replacement for the paper-based questionnaire. Conclusions This study indicates that the mobile app is a valid tool for measuring the quality of life and is as reliable as the paper-based version of the EQ-5D-5L, while reducing the response burden.
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Affiliation(s)
- Regina J M Kamstra
- Netherlands Organization for Applied Scientific Research (TNO), Zeist, Netherlands
| | - André Boorsma
- Netherlands Organization for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Tanja Krone
- Netherlands Organization for Applied Scientific Research (TNO), Utrecht, Netherlands
| | - Robin M van Stokkum
- Netherlands Organization for Applied Scientific Research (TNO), Utrecht, Netherlands
| | - Hannah M Eggink
- Netherlands Organization for Applied Scientific Research (TNO), Zeist, Netherlands
| | | | - Wilrike J Pasman
- Netherlands Organization for Applied Scientific Research (TNO), Zeist, Netherlands
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Dlima SD, Shevade S, Menezes SR, Ganju A. Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2022; 3:e39618. [PMID: 38935947 PMCID: PMC11135220 DOI: 10.2196/39618] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 06/29/2024]
Abstract
BACKGROUND Digital phenotyping is the real-time collection of individual-level active and passive data from users in naturalistic and free-living settings via personal digital devices, such as mobile phones and wearable devices. Given the novelty of research in this field, there is heterogeneity in the clinical use cases, types of data collected, modes of data collection, data analysis methods, and outcomes measured. OBJECTIVE The primary aim of this scoping review was to map the published research on digital phenotyping and to outline study characteristics, data collection and analysis methods, machine learning approaches, and future implications. METHODS We utilized an a priori approach for the literature search and data extraction and charting process, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews). We identified relevant studies published in 2020, 2021, and 2022 on PubMed and Google Scholar using search terms related to digital phenotyping. The titles, abstracts, and keywords were screened during the first stage of the screening process, and the second stage involved screening the full texts of the shortlisted articles. We extracted and charted the descriptive characteristics of the final studies, which were countries of origin, study design, clinical areas, active and/or passive data collected, modes of data collection, data analysis approaches, and limitations. RESULTS A total of 454 articles on PubMed and Google Scholar were identified through search terms associated with digital phenotyping, and 46 articles were deemed eligible for inclusion in this scoping review. Most studies evaluated wearable data and originated from North America. The most dominant study design was observational, followed by randomized trials, and most studies focused on psychiatric disorders, mental health disorders, and neurological diseases. A total of 7 studies used machine learning approaches for data analysis, with random forest, logistic regression, and support vector machines being the most common. CONCLUSIONS Our review provides foundational as well as application-oriented approaches toward digital phenotyping in health. Future work should focus on more prospective, longitudinal studies that include larger data sets from diverse populations, address privacy and ethical concerns around data collection from consumer technologies, and build "digital phenotypes" to personalize digital health interventions and treatment plans.
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Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. Eur Neuropsychopharmacol 2022; 60:100-116. [PMID: 35671641 DOI: 10.1016/j.euroneuro.2022.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 12/23/2022]
Abstract
Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.
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Clay I, Cormack F, Fedor S, Foschini L, Gentile G, van Hoof C, Kumar P, Lipsmeier F, Sano A, Smarr B, Vandendriessche B, De Luca V. Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint. J Med Internet Res 2022; 24:e35951. [PMID: 35617003 PMCID: PMC9185357 DOI: 10.2196/35951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/14/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.
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Affiliation(s)
- Ieuan Clay
- Digital Medicine Society, Boston, MA, United States
| | | | | | | | | | | | | | | | - Akane Sano
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Benjamin Smarr
- Department of Bioengineering and Halicioglu Data Science Institute, University of California, San Diego, San Diego, CA, United States
| | | | - Valeria De Luca
- Novartis Institutes for Biomedical Research, Basel, Switzerland
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Biagianti B. What Can Mobile Sensing and Assessment Strategies Capture About Human Subjectivity? Front Digit Health 2022; 4:871133. [PMID: 35493531 PMCID: PMC9051043 DOI: 10.3389/fdgth.2022.871133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Bruno Biagianti
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- *Correspondence: Bruno Biagianti
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Roefs A, Fried EI, Kindt M, Martijn C, Elzinga B, Evers AW, Wiers RW, Borsboom D, Jansen A. A new science of mental disorders: Using personalised, transdiagnostic, dynamical systems to understand, model, diagnose and treat psychopathology. Behav Res Ther 2022; 153:104096. [DOI: 10.1016/j.brat.2022.104096] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 03/29/2022] [Accepted: 04/08/2022] [Indexed: 12/18/2022]
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Liu T, Meyerhoff J, Eichstaedt JC, Karr CJ, Kaiser SM, Kording KP, Mohr DC, Ungar LH. The relationship between text message sentiment and self-reported depression. J Affect Disord 2022; 302:7-14. [PMID: 34963643 PMCID: PMC8912980 DOI: 10.1016/j.jad.2021.12.048] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/15/2021] [Accepted: 12/18/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND Personal sensing has shown promise for detecting behavioral correlates of depression, but there is little work examining personal sensing of cognitive and affective states. Digital language, particularly through personal text messages, is one source that can measure these markers. METHODS We correlated privacy-preserving sentiment analysis of text messages with self-reported depression symptom severity. We enrolled 219 U.S. adults in a 16 week longitudinal observational study. Participants installed a personal sensing app on their phones, which administered self-report PHQ-8 assessments of their depression severity, collected phone sensor data, and computed anonymized language sentiment scores from their text messages. We also trained machine learning models for predicting end-of-study self-reported depression status using on blocks of phone sensor and text features. RESULTS In correlation analyses, we find that degrees of depression, emotional, and personal pronoun language categories correlate most strongly with self-reported depression, validating prior literature. Our classification models which predict binary depression status achieve a leave-one-out AUC of 0.72 when only considering text features and 0.76 when combining text with other networked smartphone sensors. LIMITATIONS Participants were recruited from a panel that over-represented women, caucasians, and individuals with self-reported depression at baseline. As language use differs across demographic factors, generalizability beyond this population may be limited. The study period also coincided with the initial COVID-19 outbreak in the United States, which may have affected smartphone sensor data quality. CONCLUSIONS Effective depression prediction through text message sentiment, especially when combined with other personal sensors, could enable comprehensive mental health monitoring and intervention.
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Affiliation(s)
- Tony Liu
- Department of Computer and Information Science, University of Pennsylvania, USA.
| | - Jonah Meyerhoff
- Center for Behavioral Intervention Technologies (CBITs), Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, USA
| | | | | | - Susan M Kaiser
- Center for Behavioral Intervention Technologies (CBITs), Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, USA
| | - Konrad P Kording
- Department of Bioengineering, Department of Neuroscience, University of Pennsylvania, USA
| | - David C Mohr
- Center for Behavioral Intervention Technologies (CBITs), Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, USA
| | - Lyle H Ungar
- Department of Computer and Information Science, University of Pennsylvania, USA
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Schultebraucks K, Yadav V, Shalev AY, Bonanno GA, Galatzer-Levy IR. Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood. Psychol Med 2022; 52:957-967. [PMID: 32744201 DOI: 10.1017/s0033291720002718] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). METHODS N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally. RESULTS Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82). CONCLUSIONS Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.
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Affiliation(s)
- Katharina Schultebraucks
- Department of Emergency Medicine, Vagelos School of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, USA
- Data Science Institute, Columbia University, New York, New York, USA
| | | | - Arieh Y Shalev
- Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, USA
| | - George A Bonanno
- Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, New York, USA
| | - Isaac R Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, USA
- AiCure, New York, New York, USA
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Kamath J, Leon Barriera R, Jain N, Keisari E, Wang B. Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives. World J Psychiatry 2022; 12:393-409. [PMID: 35433319 PMCID: PMC8968499 DOI: 10.5498/wjp.v12.i3.393] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/23/2021] [Accepted: 02/13/2022] [Indexed: 02/06/2023] Open
Abstract
Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.
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Affiliation(s)
- Jayesh Kamath
- Department of Psychiatry and Immunology, University of Connecticut School of Medicine, University of Connecticut Health Center, Farmington, CT 06030, United States
- Department of Psychiatry, University of Connecticut School of Medicine, University of Connecticut Health Center, Farmington, CT 06032, United States
| | - Roberto Leon Barriera
- Department of Psychiatry, University of Connecticut School of Medicine, University of Connecticut Health Center, Farmington, CT 06032, United States
| | - Neha Jain
- Department of Psychiatry, University of Connecticut School of Medicine, University of Connecticut Health Center, Farmington, CT 06032, United States
| | - Efraim Keisari
- Department of Psychiatry, University of Connecticut School of Medicine, University of Connecticut Health Center, Farmington, CT 06032, United States
| | - Bing Wang
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, United States
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75
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Khedraki R, Srivastava AV, Bhavnani SP. Framework for Digital Health Phenotypes in Heart Failure. Heart Fail Clin 2022; 18:223-244. [DOI: 10.1016/j.hfc.2021.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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76
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Wu T, Simonetto DA, Halamka JD, Shah VH. The digital transformation of hepatology: The patient is logged in. Hepatology 2022; 75:724-739. [PMID: 35028960 PMCID: PMC9531185 DOI: 10.1002/hep.32329] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 12/14/2022]
Abstract
The rise in innovative digital health technologies has led a paradigm shift in health care toward personalized, patient-centric medicine that is reaching beyond traditional brick-and-mortar facilities into patients' homes and everyday lives. Digital solutions can monitor and detect early changes in physiological data, predict disease progression and health-related outcomes based on individual risk factors, and manage disease intervention with a range of accessible telemedicine and mobile health options. In this review, we discuss the unique transformation underway in the care of patients with liver disease, specifically examining the digital transformation of diagnostics, prediction and clinical decision-making, and management. Additionally, we discuss the general considerations needed to confirm validity and oversight of new technologies, usability and acceptability of digital solutions, and equity and inclusivity of vulnerable populations.
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Affiliation(s)
- Tiffany Wu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A. Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - John D. Halamka
- Mayo Clinic Platform, Mayo Clinic, Rochester, Minnesota, USA
| | - Vijay H. Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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77
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Abstract
Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.
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78
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Abstract
Physical activities, according to the embodied cognition theory, are an important manifestation of cognitive functions. As a result, in this paper, the Activate Test of Embodied Cognition (ATEC) system is proposed to assess various cognitive measures. It consists of physical exercises with different variations and difficulty levels designed to provide assessment of executive and motor functions. This work focuses on obtaining human activity representation from recorded videos of ATEC tasks in order to automatically assess embodied cognition performance. A self-supervised approach is employed in this work that can exploit a small set of annotated data to obtain an effective human activity representation. The performance of different self-supervised approaches along with a supervised method are investigated for automated cognitive assessment of children performing ATEC tasks. The results show that the supervised learning approach performance decreases as the training set becomes smaller, whereas the self-supervised methods maintain their performance by taking advantage of unlabeled data.
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79
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Inomata T, Nakamura M, Iwagami M, Sung J, Nakamura M, Ebihara N, Fujisawa K, Muto K, Nojiri S, Ide T, Okano M, Okumura Y, Fujio K, Fujimoto K, Nagao M, Hirosawa K, Akasaki Y, Murakami A. Individual characteristics and associated factors of hay fever: A large-scale mHealth study using AllerSearch. Allergol Int 2022; 71:325-334. [PMID: 35105520 DOI: 10.1016/j.alit.2021.12.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/26/2021] [Accepted: 12/13/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The prevalence of hay fever, a multifactorial allergic disease, is increasing. Identifying individual characteristics and associated factors of hay fever is essential for predictive, preventive, personalized, and participatory (P4) medicine. This study aimed to identify individual characteristics and associated factors of hay fever using an iPhone application AllerSearch. METHODS This large-scale mobile health-based cross-sectional study was conducted between February 2018 and May 2020. Individuals who downloaded AllerSearch in Japan and provided a comprehensive self-assessment (general characteristics, medical history, lifestyle habits, and hay fever symptoms [score range 0-36]) were included. Associated factors of hay fever (vs. non-hay fever) and severe hay fever symptoms were identified using multivariate logistic and linear regression analyses, respectively. RESULTS Of the included 11,284 individuals, 9041 had hay fever. Factors associated with hay fever (odds ratio) included age (0.98), female sex (1.33), atopic dermatitis (1.40), history of dry eye diagnosis (1.36), discontinuation of contact lens use during hay fever season (3.34), frequent bowel movements (1.03), and less sleep duration (0.91). The factors associated with severe hay fever symptoms among individuals with hay fever (coefficient) included age (-0.104), female sex (1.329), history of respiratory disease (1.539), history of dry eye diagnosis (0.824), tomato allergy (1.346), discontinuation of contact lens use during hay fever season (1.479), smoking habit (0.614), and having a pet (0.303). CONCLUSIONS Our large-scale mobile health-based study using AllerSearch elucidated distinct hay fever presentation patterns, characteristics, and factors associated with hay fever. Our study establishes the groundwork for effective individualized interventions for P4 medicine.
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80
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Singla RK, Joon S, Shen L, Shen B. Translational Informatics for Natural Products as Antidepressant Agents. Front Cell Dev Biol 2022; 9:738838. [PMID: 35127696 PMCID: PMC8811306 DOI: 10.3389/fcell.2021.738838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.
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Affiliation(s)
- Rajeev K. Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Shikha Joon
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Li Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Bairong Shen,
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81
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Automatic Assessment of Motor Impairments in Autism Spectrum Disorders: A Systematic Review. Cognit Comput 2022. [DOI: 10.1007/s12559-021-09940-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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82
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Gibb M, Winter H, Komarzynski S, Wreglesworth NI, Innominato PF. Holistic Needs Assessment of Cancer Survivors-Supporting the Process Through Digital Monitoring of Circadian Physiology. Integr Cancer Ther 2022; 21:15347354221123525. [PMID: 36154506 PMCID: PMC9520145 DOI: 10.1177/15347354221123525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The year 2022 could represent a significant juncture in the incorporation of mHealth solutions in routine cancer care. With the recent global COVID-19 pandemic leading a surge in both observation- and intervention-based studies predominantly aimed at remote monitoring there has been huge intellectual investment in developing platforms able to provide real time analytics that are readily usable. Another fallout from the pandemic has seen record waiting times and delayed access to cancer therapies leading to exhausting pressures on global healthcare providers. It seems an opportune time to utilize this boom in platforms to offer more efficient “at home” clinical assessments and less “in department” time for patients. Here, we will focus specifically on the role of digital tools around cancer survivorship, a relevant aspect of the cancer journey, particularly benefiting from integrative approaches. Within that context a further concept will be introduced and that is of the likely upsurge in circadian-based interpretation of continuous monitoring and the engendered therapeutic modifications. Chronobiology across the 24-hour span has long been understood to control key bodily aspects and circadian dysregulation plays a significant role in the risk of cancer and also the response to therapy and therefore progressive outcome. The rapid improvement in minimally invasive monitoring devices is, in the opinion of the authors, likely to advance introducing chronobiological amendments to routine clinical practices with positive impact on cancer survivors.
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Affiliation(s)
- Max Gibb
- Cancer Services, Betsi Cadwaladr University Health Board, Bodelwyddan, UK
| | - Hannah Winter
- Respiratory Medicine, Betsi Cadwaladr University Health Board, Bangor, UK
| | | | - Nicholas I Wreglesworth
- Cancer Services, Betsi Cadwaladr University Health Board, Bodelwyddan, UK.,Bangor University, Bangor, UK
| | - Pasquale F Innominato
- Cancer Services, Betsi Cadwaladr University Health Board, Bodelwyddan, UK.,University of Warwick, Coventry, UK.,Paris-Saclay University, Villejuif, France
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83
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Nilsen P, Svedberg P, Nygren J, Frideros M, Johansson J, Schueller S. Accelerating the impact of artificial intelligence in mental healthcare through implementation science. IMPLEMENTATION RESEARCH AND PRACTICE 2022; 3:26334895221112033. [PMID: 37091110 PMCID: PMC9924259 DOI: 10.1177/26334895221112033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. Methods The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Results Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Conclusion Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant. Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.
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Affiliation(s)
| | - Petra Svedberg
- Halmstad University School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens Nygren
- Halmstad University School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | | | | | - Stephen Schueller
- Psychological Science, University of California Irvine, Irvine, CA, USA
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84
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Leightley D, Bye A, Carter B, Trevillion K, Branthonne-Foster S, Liakata M, Wood A, Ougrin D, Orben A, Ford T, Dutta R. Maximizing the positive and minimizing the negative: Social media data to study youth mental health with informed consent. Front Psychiatry 2022; 13:1096253. [PMID: 36704745 PMCID: PMC9872114 DOI: 10.3389/fpsyt.2022.1096253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 12/16/2022] [Indexed: 01/12/2023] Open
Abstract
Social media usage impacts upon the mental health and wellbeing of young people, yet there is not enough evidence to determine who is affected, how and to what extent. While it has widened and strengthened communication networks for many, the dangers posed to at-risk youth are serious. Social media data offers unique insights into the minute details of a user's online life. Timely consented access to data could offer many opportunities to transform understanding of its effects on mental wellbeing in different contexts. However, limited data access by researchers is preventing such advances from being made. Our multidisciplinary authorship includes a lived experience adviser, academic and practicing psychiatrists, and academic psychology, as well as computational, statistical, and qualitative researchers. In this Perspective article, we propose a framework to support secure and confidential access to social media platform data for research to make progress toward better public mental health.
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Affiliation(s)
- Daniel Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amanda Bye
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Ben Carter
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Kylee Trevillion
- Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Maria Liakata
- School of Electronic Engineering and Computer Science, Queens Mary University of London, London, United Kingdom
| | | | - Dennis Ougrin
- Centre for Psychiatry and Mental Health, Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amy Orben
- MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom
| | - Tamsin Ford
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Rina Dutta
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
- *Correspondence: Rina Dutta ✉
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85
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Aguayo GA, Goetzinger C, Scibilia R, Fischer A, Seuring T, Tran VT, Ravaud P, Bereczky T, Huiart L, Fagherazzi G. Methods to Generate Innovative Research Ideas and Improve Patient and Public Involvement in Modern Epidemiological Research: Review, Patient Viewpoint, and Guidelines for Implementation of a Digital Cohort Study. J Med Internet Res 2021; 23:e25743. [PMID: 34941554 PMCID: PMC8738987 DOI: 10.2196/25743] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/16/2021] [Accepted: 10/08/2021] [Indexed: 01/20/2023] Open
Abstract
Background Patient and public involvement (PPI) in research aims to increase the quality and relevance of research by incorporating the perspective of those ultimately affected by the research. Despite these potential benefits, PPI is rarely included in epidemiology protocols. Objective The aim of this study is to provide an overview of methods used for PPI and offer practical recommendations for its efficient implementation in epidemiological research. Methods We conducted a review on PPI methods. We mirrored it with a patient advocate’s viewpoint about PPI. We then identified key steps to optimize PPI in epidemiological research based on our review and the viewpoint of the patient advocate, taking into account the identification of barriers to, and facilitators of, PPI. From these, we provided practical recommendations to launch a patient-centered cohort study. We used the implementation of a new digital cohort study as an exemplary use case. Results We analyzed data from 97 studies, of which 58 (60%) were performed in the United Kingdom. The most common methods were workshops (47/97, 48%); surveys (33/97, 34%); meetings, events, or conferences (28/97, 29%); focus groups (25/97, 26%); interviews (23/97, 24%); consensus techniques (8/97, 8%); James Lind Alliance consensus technique (7/97, 7%); social media analysis (6/97, 6%); and experience-based co-design (3/97, 3%). The viewpoint of a patient advocate showed a strong interest in participating in research. The most usual PPI modalities were research ideas (60/97, 62%), co-design (42/97, 43%), defining priorities (31/97, 32%), and participation in data analysis (25/97, 26%). We identified 9 general recommendations and 32 key PPI-related steps that can serve as guidelines to increase the relevance of epidemiological studies. Conclusions PPI is a project within a project that contributes to improving knowledge and increasing the relevance of research. PPI methods are mainly used for idea generation. On the basis of our review and case study, we recommend that PPI be included at an early stage and throughout the research cycle and that methods be combined for generation of new ideas. For e-cohorts, the use of digital tools is essential to scale up PPI. We encourage investigators to rely on our practical recommendations to extend PPI in future epidemiological studies.
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Affiliation(s)
- Gloria A Aguayo
- Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Catherine Goetzinger
- Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Renza Scibilia
- Diabetes Australia, Melbourne, Australia.,Diabetogenic, Melbourne, Australia
| | - Aurélie Fischer
- Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Till Seuring
- Luxembourg Institute of Socio-Economic Research, Esch/Alzette, Luxembourg
| | - Viet-Thi Tran
- Centre of Research in Epidemiology and Statistic Sorbonne Paris Cité, National Institute of Health and Medical Research (INSERM), French National Institute for Agricultural Research (INRA), Université de Paris, Paris, France.,Centre d'Epidémiologie Clinique, Hôpital Hôtel-Dieu, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Philippe Ravaud
- Centre of Research in Epidemiology and Statistic Sorbonne Paris Cité, National Institute of Health and Medical Research (INSERM), French National Institute for Agricultural Research (INRA), Université de Paris, Paris, France.,Centre d'Epidémiologie Clinique, Hôpital Hôtel-Dieu, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Tamás Bereczky
- European Patients' Academy on Therapeutic Innovation, Brussels, Belgium
| | - Laetitia Huiart
- Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Guy Fagherazzi
- Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
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86
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Inomata T, Nakamura M, Sung J, Midorikawa-Inomata A, Iwagami M, Fujio K, Akasaki Y, Okumura Y, Fujimoto K, Eguchi A, Miura M, Nagino K, Shokirova H, Zhu J, Kuwahara M, Hirosawa K, Dana R, Murakami A. Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study. NPJ Digit Med 2021; 4:171. [PMID: 34931013 PMCID: PMC8688467 DOI: 10.1038/s41746-021-00540-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/28/2021] [Indexed: 01/01/2023] Open
Abstract
Multidimensional integrative data analysis of digital phenotyping is crucial for elucidating the pathologies of multifactorial and heterogeneous diseases, such as the dry eye (DE). This crowdsourced cross-sectional study explored a novel smartphone-based digital phenotyping strategy to stratify and visualize the heterogenous DE symptoms into distinct subgroups. Multidimensional integrative data were collected from 3,593 participants between November 2016 and September 2019. Dimension reduction via Uniform Manifold Approximation and Projection stratified the collected data into seven clusters of symptomatic DE. Symptom profiles and risk factors in each cluster were identified by hierarchical heatmaps and multivariate logistic regressions. Stratified DE subgroups were visualized by chord diagrams, co-occurrence networks, and Circos plot analyses to improve interpretability. Maximum blink interval was reduced in clusters 1, 2, and 5 compared to non-symptomatic DE. Clusters 1 and 5 had severe DE symptoms. A data-driven multidimensional analysis with digital phenotyping may establish predictive, preventive, personalized, and participatory medicine.
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Affiliation(s)
- Takenori Inomata
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan. .,Juntendo University Graduate School of Medicine, Department of Strategic Operating Room Management and Improvement, Tokyo, Japan. .,Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan. .,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan.
| | - Masahiro Nakamura
- Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan.,Precision Health, Department of Engineering, Graduate School of Bioengineering, The University of Tokyo, Tokyo, Japan
| | - Jaemyoung Sung
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,University of South Florida, Morsani College of Medicine, Tampa, FL, USA
| | - Akie Midorikawa-Inomata
- Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Kenta Fujio
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Yasutsugu Akasaki
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Strategic Operating Room Management and Improvement, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Keiichi Fujimoto
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan
| | - Atsuko Eguchi
- Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan
| | - Maria Miura
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Ken Nagino
- Juntendo University Graduate School of Medicine, Department of Hospital Administration, Tokyo, Japan
| | - Hurramhon Shokirova
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan
| | - Jun Zhu
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan
| | - Mizu Kuwahara
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
| | - Reza Dana
- Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Akira Murakami
- Juntendo University Graduate School of Medicine, Department of Ophthalmology, Tokyo, Japan.,Juntendo University Graduate School of Medicine, Department of Digital Medicine, Tokyo, Japan
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87
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Aboujaoude E, Kuss DJ, Yao MZ, Leung LW. Editorial: Online Psychology Beyond Addiction and Gaming: A Global Look at Mental Health and Internet-Related Technologies. Front Psychol 2021; 12:815013. [PMID: 34975704 PMCID: PMC8716553 DOI: 10.3389/fpsyg.2021.815013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Elias Aboujaoude
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, United States
- *Correspondence: Elias Aboujaoude
| | - Daria Joanna Kuss
- Psychology Department, Nottingham Trent University, Nottingham, United Kingdom
| | - Mike Z. Yao
- College of Media, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Louis W. Leung
- Department of Journalism and Communications, Hong Kong Shue Yan University, Hong Kong, Hong Kong SAR, China
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Vega J, Li M, Aguillera K, Goel N, Joshi E, Khandekar K, Durica KC, Kunta AR, Low CA. Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices. Front Digit Health 2021; 3:769823. [PMID: 34870271 PMCID: PMC8636712 DOI: 10.3389/fdgth.2021.769823] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 10/21/2021] [Indexed: 01/14/2023] Open
Abstract
Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide an overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Carissa A. Low
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
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Coghlan S, D'Alfonso S. Digital Phenotyping: an Epistemic and Methodological Analysis. PHILOSOPHY & TECHNOLOGY 2021; 34:1905-1928. [PMID: 34786325 PMCID: PMC8581123 DOI: 10.1007/s13347-021-00492-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 11/04/2021] [Indexed: 11/30/2022]
Abstract
Some claim that digital phenotyping will revolutionize understanding of human psychology and experience and significantly promote human wellbeing. This paper investigates the nature of digital phenotyping in relation to its alleged promise. Unlike most of the literature to date on philosophy and digital phenotyping, which has focused on its ethical aspects, this paper focuses on its epistemic and methodological aspects. The paper advances a tetra-taxonomy involving four scenario types in which knowledge may be acquired from human “digitypes” by digital phenotyping. These scenarios comprise two causal relations and a correlative and constitutive relation that can exist between information generated by digital systems/devices on the one hand and psychological or behavioral phenomena on the other. The paper describes several modes of inference involved in deriving knowledge within these scenarios. After this epistemic mapping, the paper analyzes the possible knowledge potential and limitations of digital phenotyping. It finds that digital phenotyping holds promise of delivering insight into conditions and states as well producing potentially new psychological categories. It also argues that care must be taken that digital phenotyping does not make unwarranted conclusions and is aware of potentially distorting effects in digital sensing and measurement. If digital phenotyping is to truly revolutionize knowledge of human life, it must deliver on a range of fronts, including making accurate forecasts and diagnoses of states and behaviors, providing causal explanations of these phenomena, and revealing important constituents of human conditions, psychology, and experience.
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Affiliation(s)
- Simon Coghlan
- School of Computing & Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria, Australia
| | - Simon D'Alfonso
- School of Computing & Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria, Australia
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90
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Gagnon Shaigetz V, Proulx C, Cabral A, Choudhury N, Hewko M, Kohlenberg E, Segado M, Smith MSD, Debergue P. An Immersive and Interactive Platform for Cognitive Assessment and Rehabilitation (bWell): Design and Iterative Development Process. JMIR Rehabil Assist Technol 2021; 8:e26629. [PMID: 34730536 PMCID: PMC8600432 DOI: 10.2196/26629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 07/13/2021] [Accepted: 07/27/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Immersive technologies like virtual reality can enable clinical care that meaningfully aligns with real-world deficits in cognitive functioning. However, options in immersive 3D environments are limited, partly because of the unique challenges presented by the development of a clinical care platform. These challenges include selecting clinically relevant features, enabling tasks that capture the full breadth of deficits, ensuring longevity in a rapidly changing technology landscape, and performing the extensive technical and clinical validation required for digital interventions. Complicating development, is the need to integrate recommendations from domain experts at all stages. OBJECTIVE The Cognitive Health Technologies team at the National Research Council Canada aims to overcome these challenges with an iterative process for the development of bWell, a cognitive care platform providing multisensory cognitive tasks for adoption by treatment providers. METHODS The team harnessed the affordances of immersive technologies while taking an interdisciplinary research and developmental approach, obtaining active input from domain experts with iterative deliveries of the platform. The process made use of technology readiness levels, agile software development, and human-centered design to advance four main activities: identification of basic requirements and key differentiators, prototype design and foundational research to implement components, testing and validation in lab settings, and recruitment of external clinical partners. RESULTS bWell was implemented according to the findings from the design process. The main features of bWell include multimodal (fully, semi, or nonimmersive) and multiplatform (extended reality, mobile, and PC) implementation, configurable exercises that pair standardized assessment with adaptive and gamified variants for therapy, a therapist-facing user interface for task administration and dosing, and automated activity data logging. bWell has been designed to serve as a broadly applicable toolkit, targeting general aspects of cognition that are commonly impacted across many disorders, rather than focusing on 1 disorder or a specific cognitive domain. It comprises 8 exercises targeting different domains: states of attention (Egg), visual working memory (Theater), relaxation (Tent), inhibition and cognitive control (Mole), multitasking (Lab), self-regulation (Butterfly), sustained attention (Stroll), and visual search (Cloud). The prototype was tested and validated with healthy adults in a laboratory environment. In addition, a cognitive care network (5 sites across Canada and 1 in Japan) was established, enabling access to domain expertise and providing iterative input throughout the development process. CONCLUSIONS Implementing an interdisciplinary and iterative approach considering technology maturity brought important considerations for the development of bWell. Altogether, this harnesses the affordances of immersive technology and design for a broad range of applications, and for use in both cognitive assessment and rehabilitation. The technology has attained a maturity level of prototype implementation with preliminary validation carried out in laboratory settings, with next steps to perform the validation required for its eventual adoption as a clinical tool.
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Affiliation(s)
- Vincent Gagnon Shaigetz
- Simulation and Digital Health, Medical Devices Research Centre, National Research Council Canada, Boucherville, QC, Canada
| | - Catherine Proulx
- Simulation and Digital Health, Medical Devices Research Centre, National Research Council Canada, Boucherville, QC, Canada
| | - Anne Cabral
- Simulation and Digital Health, Medical Devices Research Centre, National Research Council Canada, Boucherville, QC, Canada
| | - Nusrat Choudhury
- Simulation and Digital Health, Medical Devices Research Centre, National Research Council Canada, Boucherville, QC, Canada
| | - Mark Hewko
- Simulation and Digital Health, Medical Devices Research Centre, National Research Council Canada, Winnipeg, MB, Canada
| | - Elicia Kohlenberg
- Simulation and Digital Health, Medical Devices Research Centre, National Research Council Canada, Winnipeg, MB, Canada
| | - Melanie Segado
- Simulation and Digital Health, Medical Devices Research Centre, National Research Council Canada, Boucherville, QC, Canada
| | - Michael S D Smith
- Simulation and Digital Health, Medical Devices Research Centre, National Research Council Canada, Winnipeg, MB, Canada
| | - Patricia Debergue
- Simulation and Digital Health, Medical Devices Research Centre, National Research Council Canada, Boucherville, QC, Canada
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91
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Guni A, Normahani P, Davies A, Jaffer U. Harnessing Machine Learning to Personalize Web-Based Health Care Content. J Med Internet Res 2021; 23:e25497. [PMID: 34665146 PMCID: PMC8564651 DOI: 10.2196/25497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/19/2021] [Accepted: 03/16/2021] [Indexed: 01/23/2023] Open
Abstract
Web-based health care content has emerged as a primary source for patients to access health information without direct guidance from health care providers. The benefit of this approach is dependent on the ability of patients to access engaging high-quality information, but significant variability in the quality of web-based information often forces patients to navigate large quantities of inaccurate, incomplete, irrelevant, or inaccessible content. Personalization positions the patient at the center of health care models by considering their needs, preferences, goals, and values. However, the traditional methods used thus far in health care to determine the factors of high-quality content for a particular user are insufficient. Machine learning (ML) uses algorithms to process and uncover patterns within large volumes of data to develop predictive models that automatically improve over time. The health care sector has lagged behind other industries in implementing ML to analyze user and content features, which can automate personalized content recommendations on a mass scale. With the advent of big data in health care, which builds comprehensive patient profiles drawn from several disparate sources, ML can be used to integrate structured and unstructured data from users and content to deliver content that is predicted to be effective and engaging for patients. This enables patients to engage in their health and support education, self-management, and positive behavior change as well as to enhance clinical outcomes.
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Affiliation(s)
- Ahmad Guni
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Imperial Vascular Unit, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Pasha Normahani
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Imperial Vascular Unit, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Alun Davies
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Imperial Vascular Unit, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Usman Jaffer
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Imperial Vascular Unit, Imperial College Healthcare NHS Trust, London, United Kingdom
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92
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Boulos LJ, Mendes A, Delmas A, Chraibi Kaadoud I. An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health. Front Psychiatry 2021; 12:574440. [PMID: 34630171 PMCID: PMC8495427 DOI: 10.3389/fpsyt.2021.574440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 08/12/2021] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence (AI) algorithms together with advances in data storage have recently made it possible to better characterize, predict, prevent, and treat a range of psychiatric illnesses. Amid the rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector, the upcoming years are facing a need to homogenize research and development processes in academia as well as in the private sector and to centralize data into federalizing platforms. This has become even more important in light of the current global pandemic. Here, we propose an end-to-end methodology that optimizes and homogenizes digital research processes. Each step of the process is elaborated from project conception to knowledge extraction, with a focus on data analysis. The methodology is based on iterative processes, thus allowing an adaptation to the rate at which digital technologies evolve. The methodology also advocates for interdisciplinary (from mathematics to psychology) and intersectoral (from academia to the industry) collaborations to merge the gap between fundamental and applied research. We also pinpoint the ethical challenges and technical and human biases (from data recorded to the end user) associated with digital mental health. In conclusion, our work provides guidelines for upcoming digital mental health studies, which will accompany the translation of fundamental mental health research to digital technologies.
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93
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Tan CC, Lam CSP, Matchar DB, Zee YK, Wong JEL. Singapore's health-care system: key features, challenges, and shifts. Lancet 2021; 398:1091-1104. [PMID: 34481560 DOI: 10.1016/s0140-6736(21)00252-x] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 10/16/2020] [Accepted: 01/19/2021] [Indexed: 01/13/2023]
Abstract
Since Singapore became an independent nation in 1965, the development of its health-care system has been underpinned by an emphasis on personal responsibility for health, and active government intervention to ensure access and affordability through targeted subsidies and to reduce unnecessary costs. Singapore is achieving good health outcomes, with a total health expenditure of 4·47% of gross domestic product in 2016. However, the health-care system is contending with increased stress, as reflected in so-called pain points that have led to public concern, including shortages in acute hospital beds and intermediate and long-term care (ILTC) services, and high out-of-pocket payments. The main drivers of these challenges are the rising prevalence of non-communicable diseases and rapid population ageing, limitations in the delivery and organisation of primary care and ILTC, and financial incentives that might inadvertently impede care integration. To address these challenges, Singapore's Ministry of Health implemented a comprehensive set of reforms in 2012 under its Healthcare 2020 Masterplan. These reforms substantially increased the capacity of public hospital beds and ILTC services in the community, expanded subsidies for primary care and long-term care, and introduced a series of financing health-care reforms to strengthen financial protection and coverage. However, it became clear that these measures alone would not address the underlying drivers of system stress in the long term. Instead, the system requires, and is making, much more fundamental changes to its approach. In 2016, the Ministry of Health encapsulated the required shifts in terms of the so-called Three Beyonds-namely, beyond health care to health, beyond hospital to community, and beyond quality to value.
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Affiliation(s)
- Chorh Chuan Tan
- Office for Healthcare Transformation, Ministry of Health, Singapore; Department of Medicine, National University of Singapore, Singapore.
| | - Carolyn S P Lam
- National Heart Centre Singapore, Singapore; Duke-NUS Cardiovascular Academic Clinical Program, Duke-NUS Medical School, Singapore; Department of Cardiology, University Medical Center Groningen, Groningen, Netherlands
| | - David B Matchar
- Health Services and Systems Research, Duke-NUS Medical School, Singapore; Department of Medicine, Duke University, Durham, NC, USA
| | | | - John E L Wong
- Department of Medicine, National University of Singapore, Singapore; Department of Hematology-Medical Oncology, National University Health System, Singapore
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94
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Minen MT, Stieglitz EJ. Wearables for Neurologic Conditions: Considerations for Our Patients and Research Limitations. Neurol Clin Pract 2021; 11:e537-e543. [PMID: 34484952 DOI: 10.1212/cpj.0000000000000971] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 07/07/2020] [Indexed: 12/24/2022]
Abstract
Purpose of Review In 2019, over 50 million Americans were expected to use wearables at least monthly. The technologies have varied capabilities, with many designed to monitor health conditions. We present a narrative review to raise awareness of wearable technologies that may be relevant to the field of neurology. We also discuss the implications of these wearables for our patients and briefly discuss issues related to researching new wearable technologies. Recent Findings There are a variety of wearables for neurologic conditions, e.g., stroke (for potential arrhythmia capture), epilepsy, Parkinson disease, and sleep. Research is being performed to capture the risk of neuropsychiatric relapse. However, data are limited and adherence to these wearables is often poorly studied. Summary The care of neurology patients may ultimately be improved with the use of wearable technologies. More research needs to examine efficacy and implementation strategies.
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Affiliation(s)
- Mia T Minen
- Division of Headache Medicine (MTM), NYU Langone Departments of Neurology and Population Health, New York, NY; and CIPPA/US (EJS), New York, NY
| | - Eric J Stieglitz
- Division of Headache Medicine (MTM), NYU Langone Departments of Neurology and Population Health, New York, NY; and CIPPA/US (EJS), New York, NY
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95
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Meyerhoff J, Liu T, Kording KP, Ungar LH, Kaiser SM, Karr CJ, Mohr DC. Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study. J Med Internet Res 2021; 23:e22844. [PMID: 34477562 PMCID: PMC8449302 DOI: 10.2196/22844] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 10/29/2020] [Accepted: 07/19/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort. OBJECTIVE This study aims to evaluate whether changes in phone sensor-derived behavioral features were associated with subsequent changes in mental health symptoms. METHODS This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms. RESULTS A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (Time: r=-0.23, P=.02; Locations: r=-0.36, P<.001), exercise duration (r=0.39; P=.03) and use of active apps (r=-0.31; P<.001). Among the depression and anxiety groups, changes in depression were preceded by changes in GPS features for Locations (r=-0.20; P=.03) and Transitions (r=-0.21; P=.03). Depression changes were not related to subsequent sensor-derived features. The minimal symptoms group showed no significant relationships. There were no associations between sensor-based features and anxiety and minimal associations between sensor-based features and social anxiety. CONCLUSIONS Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms.
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Affiliation(s)
- Jonah Meyerhoff
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Tony Liu
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Konrad P Kording
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, United States
| | - Lyle H Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Susan M Kaiser
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | | | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
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96
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Martinez-Martin N, Greely HT, Cho MK. Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study. JMIR Mhealth Uhealth 2021; 9:e27343. [PMID: 34319252 PMCID: PMC8367187 DOI: 10.2196/27343] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 05/06/2021] [Accepted: 05/21/2021] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Digital phenotyping (also known as personal sensing, intelligent sensing, or body computing) involves the collection of biometric and personal data in situ from digital devices, such as smartphones, wearables, or social media, to measure behavior or other health indicators. The collected data are analyzed to generate moment-by-moment quantification of a person's mental state and potentially predict future mental states. Digital phenotyping projects incorporate data from multiple sources, such as electronic health records, biometric scans, or genetic testing. As digital phenotyping tools can be used to study and predict behavior, they are of increasing interest for a range of consumer, government, and health care applications. In clinical care, digital phenotyping is expected to improve mental health diagnoses and treatment. At the same time, mental health applications of digital phenotyping present significant areas of ethical concern, particularly in terms of privacy and data protection, consent, bias, and accountability. OBJECTIVE This study aims to develop consensus statements regarding key areas of ethical guidance for mental health applications of digital phenotyping in the United States. METHODS We used a modified Delphi technique to identify the emerging ethical challenges posed by digital phenotyping for mental health applications and to formulate guidance for addressing these challenges. Experts in digital phenotyping, data science, mental health, law, and ethics participated as panelists in the study. The panel arrived at consensus recommendations through an iterative process involving interviews and surveys. The panelists focused primarily on clinical applications for digital phenotyping for mental health but also included recommendations regarding transparency and data protection to address potential areas of misuse of digital phenotyping data outside of the health care domain. RESULTS The findings of this study showed strong agreement related to these ethical issues in the development of mental health applications of digital phenotyping: privacy, transparency, consent, accountability, and fairness. Consensus regarding the recommendation statements was strongest when the guidance was stated broadly enough to accommodate a range of potential applications. The privacy and data protection issues that the Delphi participants found particularly critical to address related to the perceived inadequacies of current regulations and frameworks for protecting sensitive personal information and the potential for sale and analysis of personal data outside of health systems. CONCLUSIONS The Delphi study found agreement on a number of ethical issues to prioritize in the development of digital phenotyping for mental health applications. The Delphi consensus statements identified general recommendations and principles regarding the ethical application of digital phenotyping to mental health. As digital phenotyping for mental health is implemented in clinical care, there remains a need for empirical research and consultation with relevant stakeholders to further understand and address relevant ethical issues.
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Affiliation(s)
- Nicole Martinez-Martin
- Center for Biomedical Ethics, School of Medicine, Stanford University, Stanford, CA, United States
| | | | - Mildred K Cho
- Center for Biomedical Ethics, School of Medicine, Stanford University, Stanford, CA, United States
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97
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De Panfilis L, Peruselli C, Tanzi S, Botrugno C. AI-based clinical decision-making systems in palliative medicine: ethical challenges. BMJ Support Palliat Care 2021; 13:183-189. [PMID: 34257065 DOI: 10.1136/bmjspcare-2021-002948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 06/28/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Improving palliative care (PC) is demanding due to the increase in people with PC needs over the next few years. An early identification of PC needs is fundamental in the care approach: it provides effective patient-centred care and could improve outcomes such as patient quality of life, reduction of the overall length of hospitalisation, survival rate prolongation, the satisfaction of both the patients and caregivers and cost-effectiveness. METHODS We reviewed literature with the objective of identifying and discussing the most important ethical challenges related to the implementation of AI-based data processing services in PC and advance care planning. RESULTS AI-based mortality predictions can signal the need for patients to obtain access to personalised communication or palliative care consultation, but they should not be used as a unique parameter to activate early PC and initiate an ACP. A number of factors must be included in the ethical decision-making process related to initiation of ACP conversations, among which are autonomy and quality of life, the risk of worsening healthcare status, the commitment by caregivers, the patients' psychosocial and spiritual distress and their wishes to initiate EOL discussions CONCLUSIONS: Despite the integration of artificial intelligence (AI)-based services into routine healthcare practice could have a positive effect of promoting early activation of ACP by means of a timely identification of PC needs, from an ethical point of view, the provision of these automated techniques raises a number of critical issues that deserve further exploration.
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Affiliation(s)
- Ludovica De Panfilis
- Bioethics Unit, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Emilia-Romagna, Italy
| | - Carlo Peruselli
- Palliative Care Unit, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Emilia-Romagna, Italy
| | - Silvia Tanzi
- Palliative Care Unit, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Emilia-Romagna, Italy
| | - Carlo Botrugno
- Research Unit on Everyday Bioethics and Ethics of Science, Department of Legal Sciences, University of Florence, Firenze, Toscana, Italy
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98
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Neethirajan S, Kemp B. Digital Phenotyping in Livestock Farming. Animals (Basel) 2021; 11:2009. [PMID: 34359137 PMCID: PMC8300347 DOI: 10.3390/ani11072009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 12/18/2022] Open
Abstract
Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals' individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.
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Affiliation(s)
- Suresh Neethirajan
- Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, 6700 AH Wageningen, The Netherlands;
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99
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Vaidyanathan S, Munoli RN, Praharaj SK, Udupa ST. Can digital phenotyping be an answer to the COVID-19 challenges in psychiatry in India? Asian J Psychiatr 2021; 61:102679. [PMID: 34010763 PMCID: PMC8111880 DOI: 10.1016/j.ajp.2021.102679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 05/06/2021] [Indexed: 12/01/2022]
Affiliation(s)
- Sivapriya Vaidyanathan
- Department of Psychiatry, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
| | - Ravindra N Munoli
- Department of Psychiatry, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
| | - Samir Kumar Praharaj
- Department of Psychiatry, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
| | - Suma T Udupa
- Department of Psychiatry, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India.
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100
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Prakash J, Chaudhury S, Chatterjee K. Digital phenotyping in psychiatry: When mental health goes binary. Ind Psychiatry J 2021; 30:191-192. [PMID: 35017799 PMCID: PMC8709510 DOI: 10.4103/ipj.ipj_223_21] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 11/04/2022] Open
Affiliation(s)
- Jyoti Prakash
- Department of Psychiatry, Armed Forces Medical College, Pune, Maharashtra, India
| | - Suprakash Chaudhury
- Department of Psychiatry, Dr. D. Y. Patil Medical College, Pune, Maharashtra, India
| | - Kaushik Chatterjee
- Department of Psychiatry, Armed Forces Medical College, Pune, Maharashtra, India
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